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XStore: Fast RDMA-Based Ordered Key-Value Store Using Remote Learned Cache

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Published:16 August 2021Publication History
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Abstract

RDMA (Remote Direct Memory Access) has gained considerable interests in network-attached in-memory key-value stores. However, traversing the remote tree-based index in ordered key-value stores with RDMA becomes a critical obstacle, causing an order-of-magnitude slowdown and limited scalability due to multiple round trips. Using index cache with conventional wisdom—caching partial data and traversing them locally—usually leads to limited effect because of unavoidable capacity misses, massive random accesses, and costly cache invalidations.

We argue that the machine learning (ML) model is a perfect cache structure for the tree-based index, termed learned cache. Based on it, we design and implement XStore, an RDMA-based ordered key-value store with a new hybrid architecture that retains a tree-based index at the server to perform dynamic workloads (e.g., inserts) and leverages a learned cache at the client to perform static workloads (e.g., gets and scans). The key idea is to decouple ML model retraining from index updating by maintaining a layer of indirection from logical to actual positions of key-value pairs. It allows a stale learned cache to continue predicting a correct position for a lookup key. XStore ensures correctness using a validation mechanism with a fallback path and further uses speculative execution to minimize the cost of cache misses. Evaluations with YCSB benchmarks and production workloads show that a single XStore server can achieve over 80 million read-only requests per second. This number outperforms state-of-the-art RDMA-based ordered key-value stores (namely, DrTM-Tree, Cell, and eRPC+Masstree) by up to 5.9× (from 3.7×). For workloads with inserts, XStore still provides up to 3.5× (from 2.7×) throughput speedup, achieving 53M reqs/s. The learned cache can also reduce client-side memory usage and further provides an efficient memory-performance tradeoff, e.g., saving 99% memory at the cost of 20% peak throughput.

References

  1. 2021. Intel’s Math Kernel Library. (2021). https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html.Google ScholarGoogle Scholar
  2. 2021. Memcached. (2021). https://memcached.org/.Google ScholarGoogle Scholar
  3. 2021. OpenStreetMap (OSM) on AWS. (2021). https://aws.amazon.com/public-datasets/osm.Google ScholarGoogle Scholar
  4. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16). USENIX Association, Savannah, GA, 265–283. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Marcos K. Aguilera, Kimberly Keeton, Stanko Novakovic, and Sharad Singhal. 2019. Designing far memory data structures: Think outside the box. In Proceedings of the Workshop on Hot Topics in Operating Systems (HotOS’19). Association for Computing Machinery, New York, NY, 120–126. DOI:https://doi.org/10.1145/3317550.3321433 Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Berk Atikoglu, Yuehai Xu, Eitan Frachtenberg, Song Jiang, and Mike Paleczny. 2012. Workload analysis of a large-scale key-value store. In Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS’12). ACM, New York, NY, 53–64. DOI:https://doi.org/10.1145/2254756.2254766 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Colin Blundell, E. Christopher Lewis, and Milo M. K. Martin. 2006. Subtleties of transactional memory atomicity semantics. IEEE Comput. Archit. Lett. 5, 2 (2006). Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Nathan Bronson, Zach Amsden, George Cabrera, Prasad Chakka, Peter Dimov, Hui Ding, Jack Ferris, Anthony Giardullo, Sachin Kulkarni, Harry C. Li, et al. 2013. TAO: Facebook’s distributed data store for the social graph. In USENIX Annual Technical Conference. 49–60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Wei Cao, Zhenjun Liu, Peng Wang, Sen Chen, Caifeng Zhu, Song Zheng, Yuhui Wang, and Guoqing Ma. 2018. PolarFS: An ultra-low latency and failure resilient distributed file system for shared storage cloud database. Proc. VLDB Endow. 11, 12 (Aug. 2018), 1849–1862. DOI:https://doi.org/10.14778/3229863.3229872 Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Benjamin Cassell, Tyler Szepesi, Bernard Wong, Tim Brecht, Jonathan Ma, and Xiaoyi Liu. 2017. Nessie: A decoupled, client-driven key-value store using RDMA. IEEE Trans. Parallel Distrib. Syst. 28, 12 (2017), 3537–3552.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Haibo Chen, Rong Chen, Xingda Wei, Jiaxin Shi, Yanzhe Chen, Zhaoguo Wang, Binyu Zang, and Haibing Guan. 2017. Fast in-memory transaction processing using RDMA and HTM. ACM Trans. Comput. Syst. 35, 1 (July 2017), Article 3, 37 pages. DOI:https://doi.org/10.1145/3092701 Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Youmin Chen, Youyou Lu, and Jiwu Shu. 2019. Scalable RDMA RPC on reliable connection with efficient resource sharing. In Proceedings of the 14th EuroSys Conference 2019 (EuroSys’19). Association for Computing Machinery, New York, NY, Article 19, 14 pages. DOI:https://doi.org/10.1145/3302424.3303968 Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Yanzhe Chen, Xingda Wei, Jiaxin Shi, Rong Chen, and Haibo Chen. 2016. Fast and general distributed transactions using RDMA and HTM. In Proceedings of the 11th European Conference on Computer Systems. ACM, 26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Brian F. Cooper. 2021. YCSB Core Workloads. (2021). https://github.com/brianfrankcooper/YCSB/wiki/Core-Workloads.Google ScholarGoogle Scholar
  15. Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, and Russell Sears. 2010. Benchmarking cloud serving systems with YCSB. In Proceedings of the 1st ACM Symposium on Cloud Computing (SoCC’10). ACM, 143–154. DOI:https://doi.org/10.1145/1807128.1807152 Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. James C. Corbett, Jeffrey Dean, Michael Epstein, Andrew Fikes, Christopher Frost, J. J. Furman, Sanjay Ghemawat, Andrey Gubarev, Christopher Heiser, Peter Hochschild, Wilson Hsieh, Sebastian Kanthak, Eugene Kogan, Hongyi Li, Alexander Lloyd, Sergey Melnik, David Mwaura, David Nagle, Sean Quinlan, Rajesh Rao, Lindsay Rolig, Yasushi Saito, Michal Szymaniak, Christopher Taylor, Ruth Wang, and Dale Woodford. 2012. Spanner: Google’s globally-distributed database. In 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI’12). USENIX Association, Hollywood, CA, 261–264. https://www.usenix.org/conference/osdi12/technical-sessions/presentation/corbett. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Yifan Dai, Yien Xu, Aishwarya Ganesan, Ramnatthan Alagappan, Brian Kroth, Andrea Arpaci-Dusseau, and Remzi Arpaci-Dusseau. 2020. From WiscKey to Bourbon: A learned index for log-structured merge trees. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI’20). USENIX Association. https://www.usenix.org/conference/osdi20/presentation/dai.Google ScholarGoogle Scholar
  18. Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Jaeyoung Do, Yinan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet, and Tim Kraska. 2020. ALEX: An updatable adaptive learned index. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD’20). Association for Computing Machinery, New York, NY, 969–984. DOI:https://doi.org/10.1145/3318464.3389711 Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Aleksandar Dragojević, Dushyanth Narayanan, Orion Hodson, and Miguel Castro. 2014. FaRM: Fast remote memory. In Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation (NSDI’14). USENIX Association, 401–414. http://dl.acm.org/citation.cfm?id=2616448.2616486. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Aleksandar Dragojević, Dushyanth Narayanan, Edmund B. Nightingale, Matthew Renzelmann, Alex Shamis, Anirudh Badam, and Miguel Castro. 2015. No compromises: Distributed transactions with consistency, availability, and performance. In Proceedings of the 25th Symposium on Operating Systems Principles (SOSP’15). ACM, New York, NY, 54–70. DOI:https://doi.org/10.1145/2815400.2815425 Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Alex Galakatos, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, and Tim Kraska. 2019. FITing-tree: A data-aware index structure. In Proceedings of the 2019 International Conference on Management of Data (SIGMOD’19). Association for Computing Machinery, New York, NY, 1189–1206. DOI:https://doi.org/10.1145/3299869.3319860 Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Goetz Graefe. 2004. Write-optimized B-trees. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB’04). VLDB Endowment, 672–683. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Chuanxiong Guo, Haitao Wu, Zhong Deng, Gaurav Soni, Jianxi Ye, Jitu Padhye, and Marina Lipshteyn. 2016. RDMA over commodity ethernet at scale. In Proceedings of the 2016 ACM SIGCOMM Conference (SIGCOMM’16). ACM, New York, NY, 202–215. DOI:https://doi.org/10.1145/2934872.2934908 Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojciech Moczydlowski, and Alexander Van Esbroeck. 2016. Monotonic calibrated interpolated look-up tables. J. Mach. Learn. Res. 17, 1 (Jan. 2016), 3790–3836. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. High-Performance Big Data (HiBD). 2021. RDMA-based Memcached (RDMA-Memcached). (2021). http://hibd.cse.ohio-state.edu.Google ScholarGoogle Scholar
  26. Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-Che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, Joao Carreira, Karl Krauth, Neeraja Yadwadkar, et al. 2019. Cloud programming simplified: A Berkeley view on serverless computing. arXiv:1902.03383. https://arxiv.org/abs/1902.03383.Google ScholarGoogle Scholar
  27. Anuj Kalia, Michael Kaminsky, and David Andersen. 2019. Datacenter RPCs can be general and fast. In 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI’19). 1–16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Anuj Kalia, Michael Kaminsky, and David G. Andersen. 2014. Using RDMA efficiently for key-value services. In Proceedings of the 2014 ACM Conference on SIGCOMM (SIGCOMM’14). ACM, 295–306. DOI:https://doi.org/10.1145/2619239.2626299 Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Anuj Kalia, Michael Kaminsky, and David G. Andersen. 2016. FaSST: Fast, scalable and simple distributed transactions with two-sided (RDMA) datagram RPCs. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16). USENIX Association, 185–201. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Anuj Kalia Michael Kaminsky and David G. Andersen. 2016. Design guidelines for high performance RDMA systems. In 2016 USENIX Annual Technical Conference. 437. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Ana Klimovic, Yawen Wang, Patrick Stuedi, Animesh Trivedi, Jonas Pfefferle, and Christos Kozyrakis. 2018. Pocket: Elastic ephemeral storage for serverless analytics. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI’18). USENIX Association, 427–444. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. 2018. The case for learned index structures. In Proceedings of the 2018 International Conference on Management of Data. ACM, 489–504. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Baptiste Lepers, Oana Balmau, Karan Gupta, and Willy Zwaenepoel. 2019. KVell: The design and implementation of a fast persistent key-value store. In Proceedings of the 27th ACM Symposium on Operating Systems Principles. 447–461. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Bojie Li, Zhenyuan Ruan, Wencong Xiao, Yuanwei Lu, Yongqiang Xiong, Andrew Putnam, Enhong Chen, and Lintao Zhang. 2017. KV-direct: High-performance in-memory key-value store with programmable NIC. In Proceedings of the 26th Symposium on Operating Systems Principles. ACM, 137–152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Hyeontaek Lim, Dongsu Han, David G. Andersen, and Michael Kaminsky. 2014. MICA: A holistic approach to fast in-memory key-value storage. In Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation (NSDI’14). USENIX Association, Berkeley, CA, 429–444. http://dl.acm.org/citation.cfm?id=2616448.2616488. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Yandong Mao, Eddie Kohler, and Robert Tappan Morris. 2012. Cache craftiness for fast multicore key-value storage. In Proceedings of the 7th ACM European Conference on Computer Systems (EuroSys’12). ACM, 183–196. DOI:https://doi.org/10.1145/2168836.2168855 Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Christopher Mitchell, Yifeng Geng, and Jinyang Li. 2013. Using one-sided RDMA reads to build a fast, CPU-efficient key-value store. In Proceedings of the 2013 USENIX Conference on Annual Technical Conference (USENIX ATC’13). USENIX Association, 103–114. http://dl.acm.org/citation.cfm?id=2535461.2535475. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Christopher Mitchell, Kate Montgomery, Lamont Nelson, Siddhartha Sen, and Jinyang Li. 2016. Balancing CPU and network in the cell distributed B-tree store. In 2016 USENIX Annual Technical Conference (USENIX ATC’16). Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Vikram Nathan, Jialin Ding, Mohammad Alizadeh, and Tim Kraska. 2020. Learning multi-dimensional indexes. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD’20). Association for Computing Machinery, New York, NY, 985–1000. DOI:https://doi.org/10.1145/3318464.3380579 Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Rajesh Nishtala, Hans Fugal, Steven Grimm, Marc Kwiatkowski, Herman Lee, Harry C. Li, Ryan McElroy, Mike Paleczny, Daniel Peek, Paul Saab, et al. 2013. Scaling memcache at Facebook. In NSDI, Vol. 13. 385–398. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Rasmus Pagh and Flemming Friche Rodler. 2004. Cuckoo hashing. J. Algorithms 51, 2 (May 2004), 122–144. DOI:https://doi.org/10.1016/j.jalgor.2003.12.002 Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8024–8035. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Herbert Robbins and Sutton Monro. 1951. A stochastic approximation method. Ann. Math. Statist. 22, 3 (1951), 400–407.Google ScholarGoogle ScholarCross RefCross Ref
  44. Alex Shamis, Matthew Renzelmann, Stanko Novakovic, Georgios Chatzopoulos, Aleksandar Dragojević, Dushyanth Narayanan, and Miguel Castro. 2019. Fast general distributed transactions with opacity. In Proceedings of the 2019 International Conference on Management of Data (SIGMOD’19). Association for Computing Machinery, New York, NY, 433–448. DOI:https://doi.org/10.1145/3299869.3300069 Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Jiaxin Shi, Youyang Yao, Rong Chen, Haibo Chen, and Feifei Li. 2016. Fast and concurrent RDF queries with RDMA-based distributed graph exploration. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI’16). USENIX Association, Berkeley, CA, 317–332. http://dl.acm.org/citation.cfm?id=3026877.3026902. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. David Sidler, Zeke Wang, Monica Chiosa, Amit Kulkarni, and Gustavo Alonso. 2020. StRoM: Smart remote memory. In Proceedings of the 15th European Conference on Computer Systems (EuroSys’20). Association for Computing Machinery, New York, NY, Article 29, 16 pages. DOI:https://doi.org/10.1145/3342195.3387519 Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Benjamin Sowell, Wojciech Golab, and Mehul A. Shah. 2012. Minuet: A scalable distributed multiversion B-tree. Proc. VLDB Endow. 5, 9 (May 2012), 884–895. DOI:https://doi.org/10.14778/2311906.2311915 Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Vikram Sreekanti, Chenggang Wu, Xiayue Charles Lin, Johann Schleier-Smith, Joseph E. Gonzalez, Joseph M. Hellerstein, and Alexey Tumanov. 2020. Cloudburst: Stateful functions-as-a-service. Proc. VLDB Endow. 13, 12 (July 2020), 2438–2452. DOI:https://doi.org/10.14778/3407790.3407836 Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Maomeng Su, Mingxing Zhang, Kang Chen, Zhenyu Guo, and Yongwei Wu. 2017. RFP: When RPC is faster than server-bypass with RDMA. In Proceedings of the 12th European Conference on Computer Systems. ACM, 1–15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Chuzhe Tang, Youyun Wang, Zhiyuan Dong, Gansen Hu, Zhaoguo Wang, Minjie Wang, and Haibo Chen. 2020. XIndex: A scalable learned index for multicore data storage. In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP’20). Association for Computing Machinery, New York, NY, 308–320. DOI:https://doi.org/10.1145/3332466.3374547 Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. The Transaction Processing Council. TPC-C Benchmark V5.11. (????). http://www.tpc.org/tpcc/.Google ScholarGoogle Scholar
  52. Shin-Yeh Tsai and Yiying Zhang. 2017. LITE kernel RDMA support for datacenter applications. In Proceedings of the 26th Symposium on Operating Systems Principles (SOSP’17). ACM, New York, NY, 306–324. DOI:https://doi.org/10.1145/3132747.3132762 Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Alexandre Verbitski, Anurag Gupta, Debanjan Saha, Murali Brahmadesam, Kamal Gupta, Raman Mittal, Sailesh Krishnamurthy, Sandor Maurice, Tengiz Kharatishvili, and Xiaofeng Bao. 2017. Amazon aurora: Design considerations for high throughput cloud-native relational databases. In Proceedings of the 2017 ACM International Conference on Management of Data. 1041–1052. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Yandong Wang, Xiaoqiao Meng, Li Zhang, and Jian Tan. 2014. C-Hint: An effective and reliable cache management for RDMA-accelerated key-value stores. In Proceedings of the ACM Symposium on Cloud Computing (SoCC’14). ACM, Article 23, 13 pages. DOI:https://doi.org/10.1145/2670979.2671002 Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Youyun Wang, Chuzhe Tang, Zhaoguo Wang, and Haibo Chen. 2020. SIndex: A scalable learned index for string keys. In Proceedings of the 11th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys’20). Association for Computing Machinery, New York, NY, 17–24. DOI:https://doi.org/10.1145/3409963.3410496Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Zhaoguo Wang, Hao Qian, Jinyang Li, and Haibo Chen. 2014. Using restricted transactional memory to build a scalable in-memory database. In Proceedings of the 9th European Conference on Computer Systems (EuroSys’14). ACM, Article 26, 15 pages. DOI:https://doi.org/10.1145/2592798.2592815 Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Xingda Wei, Zhiyuan Dong, Rong Chen, and Haibo Chen. 2018. Deconstructing RDMA-enabled distributed transactions: Hybrid is better! In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI’18). 233–251. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Xingda Wei, Jiaxin Shi, Yanzhe Chen, Rong Chen, and Haibo Chen. 2015. Fast in-memory transaction processing using RDMA and HTM. In Proceedings of the 25th Symposium on Operating Systems Principles (SOSP’15). ACM, New York, NY, 87–104. DOI:https://doi.org/10.1145/2815400.2815419 Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Xiating Xie, Xingda Wei, Rong Chen, and Haibo Chen. 2019. Pragh: Locality-preserving graph traversal with split live migration. In 2019 USENIX Annual Technical Conference (USENIX ATC’19). USENIX Association, Renton, WA, 723–738. https://www.usenix.org/conference/atc19/presentation/xie. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Seungil You, David Ding, Kevin Canini, Jan Pfeifer, and Maya Gupta. 2017. Deep lattice networks and partial monotonic functions. In Advances in Neural Information Processing Systems. 2981–2989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Erfan Zamanian, Carsten Binnig, Tim Harris, and Tim Kraska. 2017. The end of a myth: Distributed transactions can scale. Proc. VLDB Endow. 10, 6 (Feb. 2017), 685–696. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Huanchen Zhang, David G. Andersen, Andrew Pavlo, Michael Kaminsky, Lin Ma, and Rui Shen. 2016. Reducing the storage overhead of main-memory OLTP databases with hybrid indexes. In Proceedings of the 2016 International Conference on Management of Data. ACM, 1567–1581. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Tobias Ziegler, Sumukha Tumkur Vani, Carsten Binnig, Rodrigo Fonseca, and Tim Kraska. 2019. Designing distributed tree-based index structures for fast RDMA-capable networks. In Proceedings of the 2019 International Conference on Management of Data (SIGMOD’19). ACM, New York, NY, 741–758. DOI:https://doi.org/10.1145/3299869.3300081 Google ScholarGoogle ScholarDigital LibraryDigital Library

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